arXiv subjects

Alain Destexhe

Publications and source records attributed to Alain Destexhe.

At least 19 recordsLinked to original sources

arXiv2026

A class of mean-field models to bridge molecular to brain scales

Predicting how molecular changes affect large-scale brain activity is a difficult task because of the lack of appropriate methods to link scales. In this perspective, we review a class of mean-field models that can integrate biophysical details such as synaptic receptors or membrane ion channels. This leads to a multi-scale modeling approach that can be used to evaluate how microscopic changes can impact macroscopic brain activity. This approach is illustrated here for the case of anesthesia, where changes at the level of specific synaptic receptors can lead to a global change in brain activity and a disconnection from external inputs. This is only possible using mean-field models that can include enough detail about the microscopic biophysical properties. This biophysically-based mean-field approach could be generalized to study cellular or molecular origins of brain diseases, or to better understand how drugs acting at microscopic scales can influence global brain activity. Biophysical mean-field models also link different fields of neuroscience, from molecular studies to brain imaging.

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arXiv2026

Mechanistic bridges from receptors to whole-brain dynamics: mean-field reductions, validity domains, and computational trade-offs

Many pharmacological and pathological perturbations arise at molecular, synaptic, or cellular scales, but are observed through population and whole-brain signals. Cross-scale reductions must preserve relevant mechanisms while remaining tractable. This review asks which microscopic mechanisms remain explicit, interpretable, and testable after reduction, and what claims these models support. Using receptor-aware adaptive mean fields from the master-equation lineage as a worked case, we trace finite-size population statistics and semi-analytical transfer functions into conductance-based adaptive nodes coupled through the connectome. We compare this strategy with phenomenological neural masses, low-dimensional and population-density reductions, large-scale spiking models, and learned or hybrid surrogates, including computational work and memory traffic. Receptor-dependent synaptic kinetics, conductance state, and spike-frequency adaptation can remain manipulable across scales, enabling interpretable interventions and testable mesoscopic and macroscopic consequences. However, this relies on coarse-grained Markovianity, population homogeneity, quasi-stationary transfer functions, moment closure, regional uniformity, and measurement-specific observation models. First-order implementations discard covariance dynamics, while macroscopic agreement cannot identify a unique molecular cause. Node-local biological detail mainly changes prefactors, whereas dense global covariances change the scaling class. Cross-scale models should therefore be judged by the interventions and observables they preserve, validity domain, identifiability, empirical adequacy, and computational burden. Receptor-aware mean fields are not universal, but offer a transparent, tractable strategy for selected mechanistic questions when each reduction step is independently validated.

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arXiv2025

Characterizing sleep stages through the complexity-entropy plane in human intracranial data and in a whole-brain model

Characterizing the brain dynamics during different cortical states can reveal valuable information about its patterns across various cognitive processes. In particular, studying the differences between awake and sleep stages can shed light on the understanding of brain processes essential for physical and mental well-being, such as memory consolidation, information processing, and fatigue recovery. Alterations in these patterns may indicate disorders and pathologies such as obstructive sleep apnea, narcolepsy, as well as Alzheimer's and Parkinson's diseases. Here, we analyze time series obtained from intracranial recordings of 106 patients, covering four sleep stages: Wake, N2, N3, and REM. Intracranial electroencephalography (iEEG), which can include electrocorticography (ECoG) and depth recordings, represents the state-of-the-art measurements of brain activity, offering unparalleled spatial and temporal resolution for investigating neural dynamics. We characterize the signals using Bandt and Pompe symbolic methodology to calculate the Weighted Permutation Entropy (WPE) and the Statistical Complexity Measure (SCM) based on the Jensen and Shannon disequilibrium. By mapping the data onto the complexity-entropy plane, we observe that each stage occupies a distinct region, revealing its own dynamic signature. We show that our empirical results can be reproduced by a whole-brain computational model, in which each cortical region is described by a mean-field formulation based on networks of Adaptive Exponential Integrate-and-Fire (AdEx) neurons, adjusting the adaptation parameter to simulate the different sleep stages. Finally, we show that a classification approach using Support Vector Machine (SVM) provides high accuracy in distinguishing between cortical states.

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arXiv2025

Multiscale dynamical characterization of cortical brain states: from synchrony to asynchrony

The cerebral cortex spontaneously displays different patterns of activity that evolve over time according to the brain state. Sleep, wakefulness, resting states, and attention are examples of a wide spectrum of physiological states that can be sustained by the same structural network. Furthermore, additional states are generated by drugs (e.g., different levels of anesthesia) or by pathological conditions (e.g., brain lesions, disorders of consciousness). While the significance of understanding brain states in relation to brain dynamics and behavior has become increasingly evident over the past two decades, a unified definition of brain states remains elusive. In this review, we focus on two extremes of this spectrum: synchronous versus asynchronous states. These functional states predominantly underlie unconsciousness and consciousness, respectively, although exceptions exist. Our aim is to integrate data from different levels into a multiscale understanding, ranging from local circuits to whole-brain dynamics, including properties such as cortical complexity, functional connectivity, synchronization, wave propagation, and excitatory-inhibitory balance that vary across states and characterize them. Experimental and clinical data, as well as computational models (at micro-, meso-, and macrocortical levels) associated with the discussed brain states, are made available to readers.

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arXiv2023

Vast TVB parameter space exploration: A Modular Framework for Accelerating the Multi-Scale Simulation of Human Brain Dynamics

Global neural dynamics emerge from multi-scale brain structures, with neurons communicating through synapses to form transiently communicating networks. Network activity arises from intercellular communication that depends on the structure of connectome tracts and local connection, intracellular signalling cascades, and the extracellular molecular milieu that regulate cellular properties. Multi-scale models of brain function have begun to directly link the emergence of global brain dynamics in conscious and unconscious brain states to microscopic changes at the level of cells. In particular, AdEx mean-field models representing statistical properties of local populations of neurons have been connected following human tractography data to represent multi-scale neural phenomena in simulations using The Virtual Brain (TVB). While mean-field models can be run on personal computers for short simulations, or in parallel on high-performance computing (HPC) architectures for longer simulations and parameter scans, the computational burden remains high and vast areas of the parameter space remain unexplored. In this work, we report that our TVB-HPC framework, a modular set of methods used here to implement the TVB-AdEx model for GPU and analyze emergent dynamics, notably accelerates simulations and substantially reduces computational resource requirements. The framework preserves the stability and robustness of the TVB-AdEx model, thus facilitating finer resolution exploration of vast parameter spaces as well as longer simulations previously near impossible to perform. Given that simulation and analysis toolkits are made public as open-source packages, our framework serves as a template onto which other models can be easily scripted and personalized datasets can be used for studies of inter-individual variability of parameters related to functional brain dynamics.

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arXiv2022

Neuronal cable equations derived from the hydrodynamic motion of charged particles

Neuronal cable theory is usually derived from an electric analogue of the membrane, which contrasts with the slow movement of ions in aqueous media. We show here that it is possible to derive neuronal cable equations from a different perspective, based on the laws of hydrodynamic motion of charged particles (Navier-Stokes equations). This results in similar cable equations, but with additional contributions arising from nonlinear interactions inherent to fluid dynamics, and which may shape the integrative properties of the neurons.

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arXiv2021

A formalism for the long-distance magnetic field generated by populations of neurons

Brain activity can be measured using magnetic fields located at some distance from the brain, a technique called magneto-encephalography (MEG). The origin of such magnetic fields are the ionic currents involved in neuronal activity. While these processes are well known at the microscopic scale, it is less clear how large populations of neurons generate magnetic fields. Here, we attempt to make such a link by deriving a mean-field theory of magnetic field generation by populations of neurons. We use the concept of current dipoles to build a theory that can be applied to populations of neurons. We also evaluate the contributions of neuronal current sources such as the synaptic currents or action potential currents. This theory should be useful to calculate the magnetic field located at long distances from neuronal populations, such as in MEG.

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arXiv2020

Is there sufficient evidence for criticality in cortical systems?

Many studies have found evidence that the brain operates at a critical point, a processus known as self-organized criticality. A recent paper found remarkable scalings suggestive of criticality in systems as different as neural cultures, anesthetized or awake brains. We point out here that the diversity of these states would question any claimed role of criticality in information processing. Furthermore, we show that two non-critical systems pass all the tests for criticality, a control that was not provided in the original article. We conclude that such false positives demonstrate that the presence of criticality in the brain is still not proven and that we need better methods that scaling analyses.

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arXiv2020

Optimal responsiveness and collective oscillations emerging from the heterogeneity of inhibitory neurons

The brain is characterized by a strong heterogeneity of inhibitory neurons. We report that spiking neural networks display a resonance to the heterogeneity of inhibitory neurons, with optimal input/output responsiveness occurring for levels of heterogeneity similar to that found experimentally in cerebral cortex. A heterogeneous mean-field model predicts such optimal responsiveness. Moreover, we show that new dynamical regimes emerge from heterogeneity that were not present in the equivalent homogeneous system, such as sparsely synchronous collective oscillations.

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arXiv2019

Cholinergic switch between two types of slow waves in cerebral cortex

Sleep slow waves are known to participate in memory consolidation, yet slow waves occurring under anesthesia present no positive effects on memory. Here, we shed light onto this paradox, based on a combination of extracellular recordings in vivo, in vitro, and computational models. We find two types of slow waves, based on analyzing the temporal patterns of successive slow-wave events. The first type is consistently observed in natural slow-wave sleep, while the second is shown to be ubiquitous under anesthesia. Network models of spiking neurons predict that the two slow wave types emerge due to a different gain on inhibitory vs excitatory cells and that different levels of spike-frequency adaptation in excitatory cells can account for dynamical distinctions between the two types. This prediction was tested in vitro by varying adaptation strength using an agonist of acetylcholine receptors, which demonstrated a neuromodulatory switch between the two types of slow waves. Finally, we show that the first type of slow-wave dynamics is more sensitive to external stimuli, which can explain how slow waves in sleep and anesthesia differentially affect memory consolidation, as well as provide a link between slow-wave dynamics and memory diseases.

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arXiv2019

The scientific case for brain simulations

A key element of the European Union's Human Brain Project (HBP) and other large-scale brain research projects is simulation of large-scale model networks of neurons. Here we argue why such simulations will likely be indispensable for bridging the scales between the neuron and system levels in the brain, and a set of brain simulators based on neuron models at different levels of biological detail should thus be developed. To allow for systematic refinement of candidate network models by comparison with experiments, the simulations should be multimodal in the sense that they should not only predict action potentials, but also electric, magnetic, and optical signals measured at the population and system levels.

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arXiv2018

Maximum entropy models reveal the excitatory and inhibitory correlation structures in cortical neuronal activity

Maximum Entropy models can be inferred from large data-sets to uncover how collective dynamics emerge from local interactions. Here, such models are employed to investigate neurons recorded by multielectrode arrays in the human and monkey cortex. Taking advantage of the separation of excitatory and inhibitory neuron types, we construct a model including this distinction. This approach allows to shed light upon differences between excitatory and inhibitory activity across different brain states such as wakefulness and deep sleep, in agreement with previous findings. Additionally, Maximum Entropy models can also unveil novel features of neuronal interactions, which are found to be dominated by pairwise interactions during wakefulness, but are population-wide during deep sleep. In particular, inhibitory neurons are observed to be strongly tuned to the inhibitory population. Overall, we demonstrate Maximum Entropy models can be useful to analyze data-sets with classified neuron types, and to reveal the respective roles of excitatory and inhibitory neurons in organizing coherent dynamics in the cerebral cortex.

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arXiv2018

State equation from the spectral structure of human brain activity

Neural electromagnetic (EM) signals recorded non-invasively from individual human subjects vary in complexity and magnitude. Nonetheless, variation in neural activity has been difficult to quantify and interpret, due to complex, broad-band features in the frequency domain. Studying signals recorded with magnetoencephalography (MEG) from healthy young adult subjects while in resting and active states, a systematic framework inspired by thermodynamics is applied to neural EM signals. Despite considerable inter-subject variation in terms of spectral entropy and energy across time epochs, data support the existence of a robust and linear relationship defining an effective state equation, with higher energy and lower entropy in the resting state compared to active, consistently across subjects. Mechanisms underlying the emergence of relationships between empirically measured effective state functions are further investigated using a model network of coupled oscillators, suggesting an interplay between noise and coupling strength can account for coherent variation of empirically observed quantities. Taken together, the results show macroscopic neural observables follow a robust, non-trivial conservation rule for energy modulation and information generation.

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arXiv2018

Kramers-Kronig relations and the properties of conductivity and permittivity in heterogeneous media

The macroscopic electric permittivity of a given medium may depend on frequency, but this frequency dependence cannot be arbitrary, its real and imaginary parts are related by the well-known Kramers-Kronig relations. Here, we show that an analogous paradigm applies to the macroscopic electric conductivity. If the causality principle is taken into account, there exists Kramers-Kronig relations for conductivity, which are mathematically equivalent to the Hilbert transform. These relations impose strong constraints that models of heterogeneous media should satisfy to have a physically plausible frequency dependence of the conductivity and permittivity. We illustrate these relations and constraints by a few examples of known physical media. These extended relations constitute important constraints to test the consistency of past and future experimental measurements of the electric properties of heterogeneous media.

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arXiv2017

Pairwise Ising model analysis of human cortical neuron recordings

During wakefulness and deep sleep brain states, cortical neural networks show a different behavior, with the second characterized by transients of high network activity. To investigate their impact on neuronal behavior, we apply a pairwise Ising model analysis by inferring the maximum entropy model that reproduces single and pairwise moments of the neuron's spiking activity. In this work we first review the inference algorithm introduced in Ferrari,Phys. Rev. E (2016). We then succeed in applying the algorithm to infer the model from a large ensemble of neurons recorded by multi-electrode array in human temporal cortex. We compare the Ising model performance in capturing the statistical properties of the network activity during wakefulness and deep sleep. For the latter, the pairwise model misses relevant transients of high network activity, suggesting that additional constraints are necessary to accurately model the data.

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arXiv2017

A mean-field model for conductance-based networks of adaptive exponential integrate-and-fire neurons

Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at mesoscopic scales. Since VSDi signals report the average membrane potential, it seems natural to use a mean-field formalism to model such signals. Here, we investigate a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. The AdEx model can capture the spiking response of different cell types, such as regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the mean-field model. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model accurately predicts the response time course of the population. One notable exception was that the "tail" of the response at long times was not well predicted, because the mean-field does not include adaptation mechanisms. We conclude that the Master Equation formalism can yield mean-field models that predict well the behavior of nonlinear networks with conductance-based interactions and various electrophysiolgical properties, and should be a good candidate to model VSDi signals where both excitatory and inhibitory neurons contribute.

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arXiv2017

Power-law statistics and universal scaling in the absence of criticality

Critical states are sometimes identified experimentally through power-law statistics or universal scaling functions. We show here that such features naturally emerge from networks in self-sustained irregular regimes away from criticality. In these regimes, statistical physics theory of large interacting systems predict a regime where the nodes have independent and identically distributed dynamics. We thus investigated the statistics of a system in which units are replaced by independent stochastic surrogates, and found the same power-law statistics, indicating that these are not sufficient to establish criticality. We rather suggest that these are universal features of large-scale networks when considered macroscopically. These results put caution on the interpretation of scaling laws found in nature.

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arXiv2016

A framework to reconcile frequency scaling measurements, from intracellular recordings, local-field potentials, up to EEG and MEG signals

In this viewpoint article, we discuss the electric properties of the medium around neurons, which are important to correctly interpret extracellular potentials or electric field effects in neural tissue. We focus on how these electric properties shape the frequency scaling of brain signals at different scales, such as intracellular recordings, the local field potential (LFP), the electroencephalogram (EEG) or the magnetoencephalogram (MEG). These signals display frequency-scaling properties which are not consistent with resistive media. The medium appears to exert a frequency filtering scaling as $1/\sqrt{f}$, which is the typical frequency scaling of ionic diffusion. Such a scaling was also found recently by impedance measurements in physiological conditions. Ionic diffusion appears to be the only possible explanation to reconcile these measurements and the frequency-scaling properties found in different brain signals. However, other measurements suggest that the extracellular medium is essentially resistive. To resolve this discrepancy, we show new evidence that metal-electrode measurements can be perturbed by shunt currents going through the surface of the brain. Such a shunt may explain the contradictory measurements, and together with ionic diffusion, provides a framework where all observations can be reconciled. Finally, we propose a method to perform measurements avoiding shunting effects, thus enabling to test the predictions of this framework.

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